Research On Data Forwarding Strategy Based On Short-range Wireless Communication

With the rapid development of the new generation of information and communication,efficient and stable data forwarding strategy has gradually become the communication foundation of deep information interaction,and is favored by the majority of scientific workers.At present,in the increasingly complex large-scale network,the system model,architecture and key technologies of the Internet of things have not formed a clear and unified understanding,and the large-scale collaborative Internet of things is still in the theoretical research stage.Therefore,the selection and establishment of data link in complex environment has become a hot and difficult research topic.Based on the short distance wireless communication technology,this paper aims to improve the data transmission efficiency of the network under the complex background of multi parameters and heterogeneous energy.Firstly,under the background of reducing network delay,the optimal path is selected by combining Markov decision-making and ant colony algorithm to find the global optimal data transmission strategy;secondly,in the energy heterogeneous network,the unbalanced energy consumption of nodes leads to the reduction of network life cycle,and the improved gray wolf algorithm is used to enhance the local search ability of the cluster head in the network to ensure the cluster head Looking for reliability improves network performance.The specific research is as follows:1)Considering the influence of network structure,energy consumption,number of nodes,mobility and other factors on the link quality,this paper proposes a data forwarding strategy based on Markov ant colony algorithm.The method evaluates the path within the communication range of nodes by constructing a Markov based routing decision model The balance among parameters such as road quality,node residual energy and node neighbor number is dynamically adjusted to find the routing nodes that meet the actual conditions.The global optimal strategy of IOT routing link is selected according to the ant colony foraging process to make the network run in the optimal state.Compared with the traditional ant colony algorithm,this method improves the data transmission efficiency and reduces the network delay by 54.3%.2)Aiming at the problems of uneven clustering,unreasonable selection of cluster heads and direct communication between remote nodes and base stations in existing three-level energy heterogeneous wireless sensor networks,this paper proposes an ad-hgwo clustering routing protocol based on energy heterogeneous wireless sensor networks.Firstly,the protocol selects the optimal number of clusters based on the first-order energy consumption model and K-means algorithm;secondly,the wolf pack is mapped to the wireless sensor network nodes,and a new fitness function is established in each cluster considering the residual energy of nodes and the distance between the current node and the base station,and the fitness value of each node is used to select the optimal individual to approximate the optimal cluster head Finally,in order to reduce the computational complexity,a nonlinear adjustment strategy of cosine function is adopted to accelerate the convergence process of the algorithm.The simulation results of MATLAB show that the network life cycle of the algorithm is increased by about 119% compared with the traditional SEP algorithm.